MEMS enable medical innovation

Capturing motion to enable new instrumentation and diagnostic tools
Many
medical applications such as accurately determining position and
repetition rate in CPR, or the precise positioning of scanning equipment
in relation to a patient’s body, can benefit from relatively basic, yet
still precise, motion information. In these cases, a single sensor
type may be adequate, particularly if there are other sensor inputs, or
at least fixed/known boundaries to the movement and use case.

Even
with limited range of motion, or simpler motion dynamics, the
individual sensors must have well understood and controlled drift
factors, and it is often desirable to have embedded compensation within
the sensor, as well as the ability to tune it to the application via
embedded filtering.

Complex motion requires precision sensors
While
simple motion detection, linear movement along one axis, for example,
is valuable to a number of applications, such as detecting whether an
elderly person has fallen, a majority of applications involve multiple
types and multiple axes of motion. Being able to capture this complex,
multi-dimensional motion can enable new benefits while maintaining
accuracy in the most critical of environments.

In many cases,
it is necessary to combine multiple sensor types—linear and rotational,
for instance—in order to precisely determine the motion an object has
experienced. As an example, accelerometers are sensitive to the Earth’s
gravity, so they can be used to determine inclination angle. As a MEMS
accelerometer is rotated through a ±1-g field, (±90º), it is able to
translate that motion into an angle representation. However, the
accelerometer cannot distinguish static acceleration (gravity) from
dynamic acceleration. In the later case, an accelerometer can be
combined with a gyroscope, and post-processing of both devices can
discern the linear acceleration from the tilt, based upon known motion
dynamics models. This process of sensor fusion obviously becomes more
complex as the system dynamics (number of axes of motion, types, and
degrees of freedom of motion) increase.

It is also important to
understand the environmental influences on sensor accuracy. Temperature
is obviously a key concern, and can typically be corrected for; in fact
higher precision pre-calibrated sensors will dynamically compensate
themselves. A less obvious factor to consider is the potential for even
slight vibrations to produce accuracy shifts in rotational rate sensors.
These effects, known as linear acceleration and vibration
rectification, can be significant depending on the quality of the
gyroscope. Sensor fusion improves performance by using an accelerometer
to detect linear acceleration and compensate for the gyroscope’s linear
acceleration sensitivity.

For many applications, particularly
those requiring performance beyond basic pointing (up, down, left,
right) or simple movement (in motion, or stationary), multiple
degrees-of-freedom motion detection is required. For example, a six
degree-of-freedom inertial sensor has the ability to detect linear
acceleration on each of three (x, y, z) axes, and rotational movement on
the same three axis, also referred to as roll, pitch, and yaw; as
depicted in Figure 2.

Click on image to enlarge.

Fig.
2 Linear x, y and z motion, plus rotational roll, pitch and yaw make
up the six degrees of motion measurement required for full motion
assessment; often augmented by both magnetometers and a barometer.

Basic navigation principles
The
use of inertial sensors as a navigation aid has become prevalent in
industry. Typically, they are used in conjunction with other navigation
devices such as GPS. When GPS access is unreliable, inertial guidance
fills the gap in coverage with what is called dead-reckoning. Other
sensors, including optical and magnetic, may be added depending on the
environment and the performance goals. Each sensor type has its own
limitations. MEMS inertial sensors provide the potential to fully
compensate for these other sensor inaccuracies since they are not
affected by the same interferences and do not require external
infrastructure: no satellite, magnetic field, or camera is needed…just
inertia. The major navigational sensor approaches are outlined in Table II, along with their strengths and potential limitations.

As
with the potential for GPS blockage in vehicle navigation, the medical
corollary is optical guidance and the potential for line-of-sight
blockages. Inertial-based sensors perform dead-reckoning during the
optical blockage, as well as enhancing system reliability by providing
redundant sensing.

Table II: Outlined are various navigational sensors widely used in industry, and their applicability to medical navigation.

Sensor Type

Major Advantage

Potential Limitations

Applicable to Medical Navigation?

GPS

Long Term Absolute Reference

Potential Blockages

No

Magnetic

No Required Infrastructure (except Earth)

Subject to Field Interference

Limited

Optical

Intuitive

Line of Sight Obstruction

Limited

Inertial

Self-Contained

Relative, not absolute reference

Yes

One medical application outlined earlier in Table I,
involves the use of inertial sensors in the operating room for more
accurate alignment of artificial knee or hip joints with a patient’s
unique anatomical structure. The goal here is to improve joint alignment
to less than 1º error from the patient’s natural alignment axis, versus
today’s 3º or larger error using purely mechanical alignment
approaches. Greater than 95 percent of total knee arthoplasty (TKA)
procedures today are done with mechanical alignment. Computer assisted
approaches using optical alignment have only slowly begun to replace
some mechanical procedures, likely due to the equipment overhead
required.

Developing new sensors are quite challenging.The two primary challenges are clearly specified here by the Author. Out of these two the second one is, matching the sensor for a specific application is really a time consuming and interesting work. Once properly done it provides amazing results.